This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

In this paper, we present a new embedding model, called M3-Embedding, which is
distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

Retrieval-augmented in-context learning has emerged as a powerful approach for
addressing knowledge-intensive tasks using frozen language models (LM) and retrieval …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …